NETAPP

NetApp Certified AI Expert

CERTIFIED-AI-EXPERT

The NetApp Certified AI Expert certification validates your ability to architect and manage AI infrastructures built on NetApp hybrid cloud solutions. Designed for technical professionals with six to twelve months of AI experience, it covers AI concepts, lifecycle, software and hardware architecture, and common challenges. Earning it demonstrates you can deliver reliable, high-performance AI workloads.

Practice Free Questions

5Domains

31Objectives

160Practice pages

729Free questions

CERTIFIED-AI-EXPERT free practice questions

Choose any objective and open any question page — no sign-in required.

Demonstrate the ability to train and inference

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Describe machine learning benefits

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Differentiate the use between different algorithm types (supervised, unsupervised, reinforcement)

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Describe how AI is used in varied industries

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

Describe convergence of AI, high-performance computing, and analytics

31 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–31

Determine the use of AI on-premises, in the cloud, and at the edge

17 questions · 4 pages

Questions 1–5 6–10 11–15 16–17

Determine the differences between predictive AI and generative AI

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Describe the impact of predictive AI

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Describe the impact of generative text, images, videos, and decisions in Generative AI

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Determine how NetApp tools can enable data aggregating, cleansing, and modeling

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Determine the requirements needed for model generation

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

Compare the differences between model building and fine-tuning models

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Determine the requirements needed for inferencing

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Describe AI MLOps/LLMOps ecosystems and general use

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Determine the differences between Jupyter notebooks and pipelines

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Describe how the NetApp DataOps toolkit works

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Demonstrate the ability to execute AI workloads at scale with Kubernetes (Trident)

36 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–36

Describe the uses of BlueXP software tools to build AI solutions

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Describe data aggregation topologies (warehouses, data lakes, lakehouses)

15 questions · 3 pages

Questions 1–5 6–10 11–15

Describe compute architectures used with AI workloads (CPU, GPU, TPU, FPGA)

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

Describe network architecture used with AI workloads (Ethernet vs Infiniband, RDMA, GPUDirect Storage)

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Identify storage architectures used with AI workloads (C-Series, A-Series, EF-Series, StorageGRID)

8 questions · 2 pages

Questions 1–5 6–8

Determine the use cases of different protocols

12 questions · 3 pages

Questions 1–5 6–10 11–12

Determine the benefits of SuperPOD architectures with NetApp

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Describe the use cases for BasePod and OVX architectures

7 questions · 2 pages

Questions 1–5 6–7

Determine how to size storage and compute for training and inferencing workloads

18 questions · 4 pages

Questions 1–5 6–10 11–15 16–18

Describe the solutions for code, data, and model traceability

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Describe how to access and move data for AI workloads

33 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–33

Describe solutions to optimize cost

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Describe solutions to secure storage for AI workloads

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Describe solutions to maximize performance in AI workloads

36 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–36

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (31 objectives · 160 pages).

NetApp Certified AI Expert

CERTIFIED-AI-EXPERT free practice questions

Domain 1 · AI Overview20% of practice bank · 6 objectives · 148 questions20%

Domain 2 · AI Lifecycle24% of practice bank · 7 objectives · 172 questions24%

Domain 3 · AI Software Architectures20% of practice bank · 5 objectives · 144 questions20%

Domain 4 · AI Hardware Architectures14% of practice bank · 7 objectives · 102 questions14%

Domain 5 · AI Common Challenges22% of practice bank · 6 objectives · 163 questions22%